Published Paper Details:

Tomatoes are among the most essential crops with a substantial market value that get grown in huge amounts. They are widely grown and consumed not only in India but also all around the world. The main factor influencing this crop's production quality and quantity is disease. In previous studies, only the leaves of the plant were considered to identify diseases but, in some diseases, it's only the fruit that gets affected while the other parts of the plant just look fine. Identifying the disease with the naked eye sometimes leads to an inaccurate prediction, resulting in applying the wrong pesticide, which might spoil the plant. The unavailability of experts in many of the locations makes it difficult for the farmers to identify the disease. Despite experts being available in some regions, it's a time and cost-consuming process. Detecting the diseases earlier would reduce their effect on plants and raise crop productivity. Therefore, it is crucial to correctly diagnose these diseases and apply the right pesticide. An automated system can be used to solve these problems. To address this issue, we have come up with a system that uses a convolutional neural network (CNN) to identify the disease and suggests a pesticide to help eliminate that disease. This system implements a CNN since it provides the highest level of accuracy.